Homework Assignment #3

The Banana Index

Which option do you plan to pursue? Restate your question(s).

I plan to pursue option #2 that includes the infographic.

Restate your question(s).Has this changed at all since HW #1? If yes, how so?

My overarching question will be: How do other food categories greenhouse gas emissions compare to the Banana? The first underlying question will investigate the potential relationship between land use and emissions: What is the relationship between land use(kg) and emissions (kg) between all food categories? The second question will zone into food groups that have a particularly high emissions: What are the exact emissions of each food entity of the three highest (food group) emitters? The last question will discuss why the food groups are being compared to the banana: Why the banana?

Explain which variables from your data set(s) you will use to answer your question(s).

I will use a variety of variables which may require wrangling the data multiple times for each visualization. bananas_index_kg: this is a ratio of emissions efficiency based off the banana (banana = 1) emissions_kg: this is the emissions_kg in CO2-equivalent with non-CO2 with non-CO2 gases converted according to the amount of warming they cause over a 100-year timescale land_use_kg: this is the land use of each entity based off the weight of foods entity: this is the type of food food_cat: this is the category each entity belongs to I would like to note that I was unable to find a specific data set listing food categories and the foods listed in the banana index so I manually edited the data set so I am able to group by food categories of specific plots. –Let me know if this is an issue.

Load all libraries

#load all the packages and libraries needed 
library(tidyverse)
library(janitor)
library(treemap)
library(plotly)
library(packcircles)
library(ggplot2)
library(viridis)
library(ggiraph)
library(showtext)
library(patchwork)
library(sysfonts)

Load in the data set

#load in the dataset 
bananas <- read_csv(here::here("data","bananaindex.csv"))

#download Google Fonts
# Font_add_google searches google fonts repo and downloads the proper font files by name
font_add_google(name = "Khula", family = "khula")

Clean/wrangle data

#clean up your data set
#clean up the names of the bananas dataset 
clean_bananas <- bananas %>% 
  clean_names() %>%  #clean up the names 
  group_by(food_cat) %>% 
  summarize(mean = mean(bananas_index_kg))

Do some data exploration

#check out the dimensions 
#dim(bananas)

#unique(bananas$food_cat)

#Plot Circular Packing Plot

This will be the main plot that I will show in the infographic. This compares all food groups emissions to the banana.

# Add a column with the text you want to display for each bubble:
clean_bananas$text <- paste("Food Category: ",clean_bananas$food_cat, "\n", "value (kg):", clean_bananas$mean, "\n")

n_distinct(clean_bananas$food_cat)

[1] 23

# Generate the layout
packing <- circleProgressiveLayout(clean_bananas$mean, sizetype='area') #make the circle layout
data <- cbind(clean_bananas, packing) #combine your circle data to the banana data frame 
dat.gg <- circleLayoutVertices(packing, npoints=50) #adjust the size of the plot 

# create the plot initilizaing geom_polygon 
p <- ggplot() + 
  geom_polygon_interactive(data = dat.gg, aes(x,y,group = id, fill=id, tooltip = data$text[id], data_id = id), colour = "darkgrey", alpha = 0.5) + #this will create the circular plots based off food category, adjust the outline and transparency color of the plot 
  scale_fill_viridis(option = "turbo") + #set the color scheme 
  geom_text(data = data, aes(x,y, label = gsub("Group_", "", food_cat)), size=1.2, color="black", family = "serif") + #set text within the circles and edit font, and color 
  theme_void() + #remove all axes and plot background 
  theme(legend.position="none", plot.margin=unit(c(0,0,0,0),"cm")) + #set the position of the plot 
  coord_equal() + #balance coordinates 
  labs(title = "Average GHG emissions(kg) by Food Group in 2022", subtitle = "According the to Bananas") + #set up title and subtitle 
  theme(text = element_text(family = "serif"),
        plot.title.position = "plot",
    plot.title = element_text(face = "bold",
                              size = 18,
                              color = "black"), #edit the font of the title 
    plot.subtitle = element_text(size = 16,
                                 color = "darkgrey")) #edit the font of the subtitle 

# Turn it interactive
widg <- ggiraph(ggobj = p, width_svg = 7, height_svg = 5)

widg

# save the widget
# library(htmlwidgets)
# saveWidget(widg, file=paste0( getwd(), "/HtmlWidget/circular_packing_interactive.html"))

Trying to figure out how to highlight the banana circle– tips appreciated

Make a scatterplot

###The purpose of this visualization is to display the relationship between land use and emissions per kg.

#this plot needs work --tips are welcome on showing this relationship and still working on the color scheme :) 

#make a scatter plot of the relationship between land use and emissions 
w1 <- ggplot(data = bananas, aes(x = land_use_kg, y =
                                   emissions_kg)) + #call the data 
  geom_point(alpha = 0.5, color = "cornflowerblue", size = 2.5) +
  xlim(0,50) + #set the lim to take out any outliers
  ylim(0,35) +
  geom_smooth(method = "lm", se = FALSE, color = "#FFE135") + #set the best line of fit 
  labs(title = "Relationship between Emissions and Land Use(kg)", x= "Emissions(kg)", y = "Land Use(kg)") +
  theme_minimal() +
  theme(text = element_text(family = "serif"),
        plot.title = element_text(face = "bold",
                                  size = 10,
                                  color = "black")) #edit the titles and texts
w1

Bar Plot

The purpose of this visualization is to display the exact emissions of each food entity for the three highest emitters(on average)

#subset the data for meat for first bar plot 
b1 <- bananas %>%
  clean_names() %>% 
  filter(food_cat == "Meat")
#subset the data for second bar plot 
b2 <- bananas %>%
  clean_names() %>% 
  filter(food_cat == "Dairy")
#subset the data for third bar plot 
b3 <- bananas %>%
  clean_names() %>% 
  filter(food_cat == "Seafood")

# Create a bar plot of each category - meat
q1 <- ggplot(b1, aes(x = bananas_index_kg, y = entity)) +
  geom_col(fill = "indianred", alpha = 0.6) + #call the data 
  labs(y = "Entity", x = "Emissions(kg)", title = "Emissions(kg) of Meat Category") +  #label the proper titles 
  theme_classic() +
  scale_x_continuous(expand = c(0, 0)) + #this removes the space between 0 and axis 
    theme(text = element_text(family = "serif"),
          plot.title = element_text(face = "bold",
                            size = 10,
                            color = "black"),
          axis.title.x = element_text(size = 10,
                            color = "black"),
          axis.title.y = element_text(size = 10,
                            color = "black")) #edit text and theme 

# create a bar plot of each category - dairy
q2 <- ggplot(b2, aes(x = bananas_index_kg, y = entity)) + #call the data 
  geom_col(fill = "#FFE135", alpha = 0.6) + #set the color and transparency
  labs(y = "Entity", x = "Emissions(kg)", title = "Emissions(kg) of Dairy Products") + #label the axes
  theme_classic() + #remove the gridlines 
  scale_x_continuous(expand = c(0, 0)) +
    theme(text = element_text(family = "serif"),
          plot.title = element_text(face = "bold",
                            size = 10,
                            color = "black"),
          axis.title.x = element_text(size = 10,
                            color = "black"),
          axis.title.y = element_text(size = 10,
                            color = "black")) #edit the text and font 

# create a bar plot of each category - seafood 
q3 <- ggplot(b3, aes(x = bananas_index_kg, y = entity)) + #call the data 
  geom_col(fill = "cornflowerblue", alpha = 0.6) + #set color and transparency 
  labs(y = "Entity", x = "Emissions(kg)", title = "Emissions(kg) of Seafood") + #label the axes
  theme_classic() + #remove gridlines 
  scale_x_continuous(expand = c(0, 0)) + #remove the space between x and 0 
    theme(text = element_text(family = "serif"),
      plot.title = element_text(face = "bold",
                            size = 10,
                            color = "black"),
          axis.title.x = element_text(size = 10,
                            color = "black"),
          axis.title.y = element_text(size = 10,
                            color = "black"))#edit the text and font 

q1

q2

q3

#for some reason patchwork isn't working and I had issues with facet wrap so this technically counts as 1 visualization 

What challenges did you encounter or anticipate encountering as you continue to build / iterate on your visualizations in R?

As I commented in some code chunks, patchwork keeps giving me issues and doesn’t properly add my plots together. I also am running into issues with the color scheme on my circular packing plot, I am using scale_color_manual and for some reason it doesn’t register my palette. This is an issue I intend on fixing throughout the week

What ggplot extension tools / packages do you need to use to build your visualizations? Are there any that we haven’t covered in class that you’ll be learning how to use for your visualizations?

So far in my first visualization I used the package {packingcircles}, but I was able to find plenty of resources from the sites Sam listed on helping me build a plot. I don’t believe we have covered this in class, I do know that circles aren’t usually great for displaying any statistical significance, but thats what my other visualizations are for.

What feedback do you need from the instructional team and / or your peers to ensure that your intended message is clear?

I want suggestions on how to fix my color palette! Also hopping that each plot and visualization makes sense put together, if not I can adjust visualizations and maybe change a plot (most likely mys scatterplot). Mostly I hope they can understand the message I am tryign to convey in the first plot, and then build off knowledge from the second and third plots.